Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Test for Homogeneity01:23

Test for Homogeneity

2.1K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.1K
Introduction to Test of Independence01:21

Introduction to Test of Independence

2.5K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.5K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
2.4K
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

4.1K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
4.1K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

3.1K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
3.1K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

2.6K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
2.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

What Should I Do?: Information and Support Needs Relating to Substance Use on Reddit.

Substance use & misuse·2025
Same author

Identifying Stigma Phenotypes in Social Media Narratives of Substance Use: Observational Study.

Journal of medical Internet research·2025
Same author

Comparing the Use Experiences, Contextual Factors, and Recovery Strategies Associated with Different Substances: An Analysis of Social Media Narratives.

Substance use & misuse·2025
Same author

Audience Responses to Online Public Shaming in Online Environments: Mixed Methods Study.

Journal of medical Internet research·2025
Same author

User Engagement in an Online Digital Health Intervention to Promote Problem Solving.

Interacting with computers·2025
Same author

Long-read DNA and RNA sequencing for inherited polyposis and colorectal cancer: cryptic intronic variants and multiple mutational mechanisms.

Journal of medical genetics·2025
Same journal

Diagnostic Yield of Genome Sequencing in an Iranian Exome-Negative Autosomal-Recessive Intellectual Disability Cohort.

Human mutation·2026
Same journal

Exploring the Functional Impact of Individual <i>DDX41</i> Variants With a Fast and Robust Cell-Based Method.

Human mutation·2026
Same journal

Modeling the Effects of Single Nucleotide Polymorphisms (SNPs) on the Structure and Function of the Human <i>RET</i> Gene: An In Silico Study.

Human mutation·2026
Same journal

Driver Mutation Subtypes Differentially Shape Immune Evasion Landscapes in Melanoma: An AI-Driven Inflammatory Pathway Model Implicating CCNE1.

Human mutation·2026
Same journal

Comment on "When the Outcome Contains the Exposure: Methodological Limits of a Genome-Wide Cross-Trait Analysis of Type 2 Diabetes and MASLD".

Human mutation·2026
Same journal

AI-Augmented Hematological Signatures for Equitable Detection of Hereditary Hemolytic Anemia Carriers: A Global Systematic Review and Meta-Analysis.

Human mutation·2026
See all related articles

Related Experiment Video

Updated: Oct 2, 2025

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

18.6K

An algorithm for optimal testing in co-segregation analysis.

Ronald W Buie1, John Michael O Rañola2, Annie T Chen1

  • 1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.

Human Mutation
|February 28, 2022
PubMed
Summary
This summary is machine-generated.

An algorithm optimizes familial cosegregation analysis by predicting information gain from genotyping relatives. This strategy accelerates variant classification, proving more efficient in 86% of tested families compared to traditional methods.

Keywords:
cosegregation analysisfamily analysisoptimizationvariant classificationvariant of unknown significance

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.3K
Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella
07:11

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Published on: May 13, 2019

9.7K

Related Experiment Videos

Last Updated: Oct 2, 2025

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

18.6K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.3K
Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella
07:11

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Published on: May 13, 2019

9.7K

Area of Science:

  • Genetics
  • Bioinformatics
  • Medical Genomics

Background:

  • Clinical genetic sequencing frequently yields variants of uncertain significance (VUS).
  • Familial cosegregation analysis is a key method for classifying variant pathogenicity.
  • Current methods for identifying and genotyping relatives are often time-consuming and expensive.

Purpose of the Study:

  • To introduce a novel algorithm for quantifying expected information gain from genotyping additional family members.
  • To compare the efficiency of an optimized testing strategy against actual recruitment practices in cosegregation analysis.
  • To enhance the speed and accuracy of variant classification in clinical genetics.

Main Methods:

  • Development of an algorithm to measure information gain per genotyped relative.
  • Comparison of actual family recruitment strategies with algorithm-guided optimized strategies using synthetic pedigrees.
  • Calculation of likelihood ratios of pathogenicity with successive genetic tests in both actual and synthetic pedigrees.

Main Results:

  • The proposed algorithm-guided strategy achieved maximal information gain more rapidly in 30 out of 35 (86%) families studied.
  • Significant differences were observed in the rate of increase of the cosegregation likelihood ratio between actual and optimized testing approaches.
  • The optimized strategy demonstrated a more efficient progression towards variant classification.

Conclusions:

  • The presented algorithm provides a data-driven approach to optimize relative selection and genotyping for cosegregation analysis.
  • Implementation of this algorithm can lead to more efficient variant classification in clinical and research settings.
  • This method supports streamlined genetic testing and interpretation, improving diagnostic yield.